Design Criteria for a Modular Tissue-Engineered Construct
Bibliographic record
Abstract
A modular construct, created by the assembly of discrete microscale objects, has been proposed to enable the engineering of large, vascularized tissues containing multiple cell types. A simple theoretical analysis of the design constraints relevant to a modular construct was performed and used to define useable device operating ranges. The analysis assumed that the primary design constraint was the operating wall shear stress that would lead to a non-thrombogenic endothelial cell layer. At the lower end of the desirable shear range, oxygen depletion (over the length of the construct) limited the maximum allowed construct length, whereas at the upper end of this shear range, construct pressure difference limited maximum construct length. To compare with the theoretical analysis, real constructs were assembled, and construct porosity was assessed using superficial velocity-pressure difference profiles. Significant deviations from ideal construct porosity were observed for soft collagen gel constructs. Improvement of the module mechanical properties through the use of poloxamine instead of collagen as the module material enabled constructs closer to the ideal case to be assembled. With such improvements, modular tissue engineering offers a feasible strategy for the development of clinically significant whole-organ replacements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".